Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame.

Description

Although the cor function finds the correlations for a matrix, it does not report probability values. corr.test uses cor to find the correlations for either complete or pairwise data and reports the sample sizes and probability values as well. For symmetric matrices, raw probabilites are reported below the diagonal and correlations adjusted for multiple comparisons above the diagonal. In the case of different x and ys, the default is to adjust the probabilities for multiple tests.

Arguments

use="pairwise" is the default value and will do pairwise deletion of cases. use="complete" will select just complete cases.

method

method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall"

adjust

What adjustment for multiple tests should be used? ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"). See p.adjust for details about why to use "holm" rather than "bonferroni").

alpha

alpha level of confidence intervals

r

A correlation matrix

n

Number of observations if using corr.p. May be either a matrix (as returned from corr.test, or a scaler. Set to n- np if finding the significance of partial correlations. (See below).

Details

corr.test uses the cor function to find the correlations, and then applies a t-test to the individual correlations using the formula

t = r* sqrt(n-2)/sqrt(1-r^2)

se = sqrt((1-r^2)/(n-2))

The t and Standard Errors are returned as objects in the result, but are not normally displayed. Confidence intervals are found and printed if using the print(short=FALSE) option. These are found by using the fisher z transform of the correlation, and the standard error of the z transforms is

se = sqrt(1/(n-3))

.

The probability values may be adjusted using the Holm (or other) correction. If the matrix is symmetric (no y data), then the original p values are reported below the diagonal and the adjusted above the diagonal. Otherwise, all probabilities are adjusted (unless adjust="none"). This is made explicit in the output.

corr.p may be applied to the results of partial.r if n is set to n - s (where s is the number of variables partialed out) Fisher, 1924.

Value

r

The matrix of correlations

n

Number of cases per correlation

t

value of t-test for each correlation

p

two tailed probability of t for each correlation. For symmetric matrices, p values adjusted for multiple tests are reported above the diagonal.

se

standard error of the correlation

ci

the alpha/2 lower and upper values

See Also

cor.test for tests of a single correlation, Hmisc::rcorr for an equivalant function, r.test to test the difference between correlations, and cortest.mat to test for equality of two correlation matrices.

Also see cor.ci for bootstrapped confidence intervals of Pearson, Spearman, Kendall, tetrachoric or polychoric correlations. In addition cor.ci will find bootstrapped estimates of composite scales based upon a set of correlations (ala cluster.cor).

In particular, see p.adjust for a discussion of p values associated with multiple tests.

Other useful functions related to finding and displaying correlations include lowerCor for finding the correlations and then displaying the lower off diagonal using the lowerMat function. lowerUpper to compare two correlation matrices.

Examples

ct <- corr.test(attitude) #find the correlations and give the probabilities
ct #show the results
corr.test(attitude[1:3],attitude[4:6]) #reports all values corrected for multiple tests
#corr.test(sat.act[1:3],sat.act[4:6],adjust="none") #don't adjust the probabilities
#take correlations and show the probabilities as well as the confidence intervals
print(corr.p(cor(attitude[1:4]),30),short=FALSE)
#don't adjust the probabilities
print(corr.test(sat.act[1:3],sat.act[4:6],adjust="none"),short=FALSE)